GROMACS Software Research Articles

Exploring the orphan immune receptor TREM2 and its non-protein ligands: In silico characterization

The triggering receptor expressed on myeloid cells 2 (TREM2) is an immunoreceptor that interacts with a wide range of non-protein ligands, and it has been implicated in infectious and non-infectious diseases. However, there is a limited understanding on how this receptor interacts with non-protein ligands and the potential of such information to develop new therapeutic drugs. Therefore, our study aimed to elucidate the interactions between TREM2 and its non-protein ligands. First, we searched PubChem and Protein Data Bank (PDB) for TREM2 structures and their corresponding non-protein ligands. Subsequently, these structures were employed in molecular docking and MM/GBSA simulations with the Maestro software and molecular dynamics in GROMACS software. TREM2 was subsequently subjected to druggable site prediction using CavityPlus and receptor-based drug repositioning via the DrugRep server. TREM2 interacts with high affinity with its 12 non-protein ligands, with affinity values ranging from −33.01 kcal/mol for phosphatidylserine to −80.87 kcal/mol for cardiolipin (CLP). In molecular dynamics simulations, homodimeric TREM2 bound more stably to its lipid ligands, such as CLP and PSF, whereas it was unstable when unbound. The interactions between the receptor and its non-protein ligands were driven by the complementarity determining regions (CDR) 1 and 2, that are present in the hydrophobic and positively charged regions, highlighting that the Y38–R98 region is fundamental for drugs targeting TREM2. Our data underscore the significance of TREM2's CDRs in recognizing its ligands, suggesting they as promising targets for prospective drug design studies.

Pharmacophore-based 3D-QSAR modeling, virtual screening, docking, molecular dynamics and biological evaluation studies for identification of potential inhibitors of alpha-glucosidase.

Alpha-glucosidase enzyme is considered an important therapeutic target for controlling hyperglycemia associated with type 2 diabetes. Novel scaffolds identified as potential alpha-glucosidase inhibitors from the Maybridge library utilizing pharmacophore modeling, molecular docking and biological evaluation are reported in this manuscript. A total of 51 xanthone series scaffolds previously reported as alpha-glucosidase inhibitors were collected and used as training and test sets. These sets were employed to develop and validate a pharmacophore-based 3D-QSAR model with statistically meaningful results using Schrodinger software. The model showed a high F value (F, 80.1) at five component partial least square factors, a high cross-validation coefficient (Q2, 0.66) and a good correlation coefficient (R2, 0.95). Pearson correlation coefficient (r) of 0.8400 indicated a greater degree of confidence in the model. Subsequently, virtual screening was performed with PHASE module of Schrodinger software using the above model to identify novel alpha-glucosidase inhibitors, and mapped compounds were evaluated for their interactions with the protein. The X-ray co-crystallised structure of the alpha-glucosidase protein in complex with acarbose (PDB Code: 5NN8) was used for molecular docking analysis using GLIDE module and a total of eight compounds were further selected for biological evaluation. Molecular dynamics analysis using GROMACS software was performed in the active site of alpha-glucosidase protein to gain insights into binding mechanism of the four active compounds which were finally found to exhibit inhibitory activity in the biological assay.

SMARTINI3 parametrization of multi-scale membrane models via unsupervised learning methods

In this study, we utilize genetic algorithms to develop a realistic implicit solvent ultra-coarse-grained (ultra-CG) membrane model comprising only three interaction sites. The key philosophy of the ultra-CG membrane model SMARTINI3 is its compatibility with realistic membrane proteins, for example, modeled within the Martini coarse-grained (CG) model, as well as with the widely used GROMACS software for molecular simulations. Our objective is to parameterize this ultra-CG model to accurately reproduce the experimentally observed structural and thermodynamic properties of Phosphatidylcholine (PC) membranes in real units, including properties such as area per lipid, area compressibility, bending modulus, line tension, phase transition temperature, density profile, and radial distribution function. In our example, we specifically focus on the properties of a POPC membrane, although the developed membrane model could be perceived as a generic model of lipid membranes. To optimize the performance of the model (the fitness), we conduct a series of evolutionary runs with diverse random initial population sizes (ranging from 96 to 384). We demonstrate that the ultra-CG membrane model we developed exhibits authentic lipid membrane behaviors, including self-assembly into bilayers, vesicle formation, membrane fusion, and gel phase formation. Moreover, we demonstrate compatibility with the Martini coarse-grained model by successfully reproducing the behavior of a transmembrane domain embedded within a lipid bilayer. This facilitates the simulation of realistic membrane proteins within an ultra-CG bilayer membrane, enhancing the accuracy and applicability of our model in biophysical studies.

Structural investigation, computational analysis, and theoretical cryoprotectant approach of antifreeze protein type IV mutants.

Antifreeze proteins (AFPs) have unique features to sustain life in sub-zero environments due to ice recrystallization inhibition (IRI) and thermal hysteresis (TH). AFPs are in demand as agents in cryopreservation, but some antifreeze proteins have low levels of activity. This research aims to improve the cryopreservation activity of an AFPIV. In this in silico study, the helical peptide afp1m from an Antarctic yeast AFP was modeled into a sculpin AFPIV, to replace each of its four α-helices in turn, using various computational tools. Additionally, a new linker between the first two helices of AFPIV was designed, based on a flounder AFPI, to boost the ice interaction activity of the mutants. Bioinformatics tools such as ExPASy Prot-Param, Pep-Wheel, SOPMA, GOR IV, Swiss-Model, Phyre2, MODFOLD, MolPropity, and ProQ were used to validate and analyze the structural and functional properties of the model proteins. Furthermore, to evaluate the AFP/ice interaction, molecular dynamics (MD) simulations were executed for 20, 100, and 500 ns at various temperatures using GROMACS software. The primary, secondary, and 3D modeling analysis showed the best model for a redesigned antifreeze protein (AFP1mb, with afp1m in place of the fourth AFPIV helix) with a QMEAN (Swiss-Model) Z score value of 0.36, a confidence of 99.5%, a coverage score of 22%, and a p value of 0.01. The results of the MD simulations illustrated that AFP1mb had more rigidity and better ice interactions as a potential cryoprotectant than the other models; it also displayed enhanced activity in limiting ice growth at different temperatures.

Introductory Tutorials for Simulating Protein Dynamics with GROMACS.

Atomistic molecular dynamics (MD) simulations have become an indispensable tool for investigating the structure, dynamics, and energetics of biomolecules. Continual optimization of software algorithms and hardware has enabled investigators to access biologically relevant time scales in feasible amounts of computing time. Given the widespread use and utility of MD simulations, there is considerable interest in learning essential skills in performing them. Here, we present a set of introductory tutorials for performing MD simulations of proteins in the popular, open-source GROMACS package. Three exercises are detailed, including simulating a single protein, setting up a protein complex, and performing umbrella sampling simulations to model the unfolding of a short polypeptide. Essential features and input settings are illustrated throughout. The purpose of these tutorials is to provide new users with a general understanding of foundational workflows, from which they can design their own simulations.

Exploration of small molecule compounds targeting abdominal aortic aneurysm based on CMap database and molecular dynamics simulation.

The mitigation of abdominal aortic aneurysm (AAA) growth through pharmaceutical intervention offers the potential to avert the perils associated with AAA rupture and the subsequent need for surgical intervention. Nevertheless, the existing effective drugs for AAA treatment are limited, necessitating a pressing exploration for novel therapeutic medications. AAA-related transcriptome data were downloaded from GEO, and differentially expressed genes (DEGs) in AAA tissue were screened for GO and KEGG enrichment analyses. Small molecule compounds and their target proteins with negative connectivity to the AAA expression profile were predicted in the Connectivity Map (CMap) database. Molecular docking and molecular dynamics simulation were performed to predict the binding of the target protein to the small molecule compound, and the MM/GBSA method was used to calculate the binding free energy. Cluster analysis was performed using the cluster tool in the GROMACS package. An AAA cell-free model was built, and CETSA experiments were used to demonstrate the binding ability of small molecules to the target protein in cells. A total of 2244 DEGs in AAA were obtained through differential analysis, and the DEGs were mainly enriched in the tubulin binding biological function and cell cycle pathway. The CMap results showed that Apicidin had a potential therapeutic effect on AAA with a connectivity score of -97.74, and HDAC4 was the target protein of Apicidin. Based on literature, HDAC4-Apicidin was selected as the subsequent research object. The lowest affinity of Apicidin-HDAC4 molecular docking was -8.218kcal/mol. Molecular dynamics simulation results indicated that Apicidin-HDAC4 could form a stable complex. MM/GBSA analysis showed a total binding free energy of -55.40 ± 0.79kcal/mol, and cluster analysis showed that there were two main conformational clusters during the binding process, accounting for 22.4% and 57.8%, respectively. Apicidin could form hydrogen bonds with surrounding residues for stable binding. CETSA experiment proved the stable binding ability of Apicidin and HDAC4. Apicidin inhibited HDAC4 in AAA and exhibited favorable protein-ligand interactions and stability, making it a potential candidate drug for treating AAA.

Integrating serum pharmacochemistry, network pharmacology and untargeted metabolomics strategies to reveal the material basis and mechanism of action of Feining keli in the treatment of chronic bronchitis

Ethnopharmacological relevanceFeining keli (FNKL) is herbal preparation mainly made from Senecio cannabifolius Less., In recent years, more and more studies have found that FNKL has excellent therapeutic effects on chronic bronchitis (CB). Nevertheless, its pharmacodynamic material basis and mechanism of action are still unknown. Aim of the studyThis study aimed to explore the pharmacodynamic material basis and mechanism of action of FNKL in treating CB. Materials and methodsThe CB rat model was induced using nasal drops of lipopolysaccharide (LPS) in combination with smoking. Various assessments including behavioral and body mass examination, lung index measurement, enzyme linked immunosorbent assay (ELISA), as well as histological analyses using hematoxylin and eosin (H&E) and Masson staining were conducted to validate the reliability of the CB model. The serum components of FNKL in CB rats were identified using ultra-high-performance liquid chromatography Orbitrap Exploris mass spectrometer (UHPLC-OE-MS). Network pharmacology was used to predict the network of action of the active ingredients in FNKL based on these serum components. Signaling pathways were enriched and analyzed, and molecular docking was conducted for key targets. Molecular dynamics simulations were performed using GROMACS software. The mechanism was confirmed through a series of experiments including Western blot (WB), immunofluorescence (IF), and reverse transcription (RT)-PCR. Additionally, untargeted metabolomics was employed to identify biomarkers and relevant metabolic pathways associated with the treatment of CB with FNKL. ResultsIn CB rats, FNKL improved body mass, lung index, and pathological damage of lung tissues. It also decreased interleukin (IL)-6, tumor necrosis factor-alpha (TNF-α), malonaldehyde (MDA) levels, and percentage of lung collagen fiber area. Furthermore, FNKL increased IL-10 and superoxide dismutase (SOD) levels, which helped alleviate bronchial inflammation in the lungs. A total of 70 FNKL chemical components were identified in CB rat serum. Through network pharmacology analysis, 5 targets, such as PI3K, AKT, NF-κB, HIF-1α, and MYD88, were identified as key targets of FNKL in the treatment of CB. Additionally, the key signaling pathways identified were PI3K/AKT pathway、NF-κB/MyD88 pathway、HIF-1α pathway. WB, IF, and RT-PCR experiments were conducted to confirm the findings. Molecular docking studies demonstrated successful docking of 16 potential active components with 5 key targets. Additionally, molecular dynamics simulations indicated the stability of quercetin-3-galactoside and HIF-1α. Metabolomics analysis revealed that FNKL primarily regulated pathways related to alpha-linolenic acid metabolism, primary bile acid biosynthesis, bile secretion, arachidonic acid metabolism, neuroactive ligand-receptor interaction, and folate biosynthesis. Furthermore, the expression levels of traumatic acid, traumatin, alpha linolenic acid, cholic acid, 2-arachidonoylglycerol, deoxycholic acid, 7,8-dihydroneopterin, and other metabolites were found to be regulated. ConclusionFNKL exhibits positive therapeutic effects on CB, with quercetin-3-galactoside identified as a key active component. The mechanism of FNKL's therapeutic action on CB involves reducing inflammatory response, oxidative stress, and regulating metabolism, and its molecular mechanism was better elucidated in a holistic manner. This study serves as a reference for understanding the pharmacodynamic material basis and mechanism of action of FNKL in treating CB, and provides avenues for exploring the effects of compounded herbal medicines on CB.

Web-based applications for automated generation of functionalized graphene and carbon nanotube for molecular dynamics simulations and automated three-dimensional analysis of ion flow through nanopores

Functionalized graphene and carbon nanotubes (CNTs) are widely recognized for their exceptional physical properties, which make them highly suitable for various applications. Although molecular dynamics (MD) simulations are essential for investigating the atomic-level interactions and transport phenomena in functionalized graphene and CNT systems, setting up these simulations remains complex and time-consuming. To streamline this process, we have developed a novel web application that automates the generation of MD simulation setups of functionalized graphene and CNT systems compatible with AMBER force fields and the Gromacs software. Key features include the creation of nanopores, functionalization with hydrogen, hydroxyl, and/or carboxylate groups, and the application of periodic boundary conditions to effectively simulate infinite structures. To facilitate the MD analysis of transport phenomena through nanopores, our web application offers an automated analysis tool that generates and visualizes three-dimensional local flux fields from MD trajectories. Overall, our web applications significantly enhance the accessibility and efficiency of MD simulations of functionalized graphene systems, particularly for nanopore applications. Our web applications are freely available at https://yoo.skku.edu/apps.

Three-Dimensional Structure of Human Epididymis Protein 4 (HE4): A Protein Modelling of an Ovarian Cancer Biomarker Through In Silico Approach

The Human Epididymis Protein 4 (HE4) biomarker has been extensively investigated for its potential in diagnosing ovarian cancer (OC). For the application of diagnostic techniques and drug delivery, it is crucial to understand the protein tertiary structure. However, the Protein Data Bank (PDB) does not currently contain the three-dimensional (3D) structure of HE4. Therefore, an in silico analysis was conducted to model the HE4 protein using AlphaFold, I-TASSER, and Robetta servers, with the sequence retrieved from UniProt (ID: Q14508). These three servers employed deep learning algorithms, threading templates, and de novo methods, respectively. Subsequently, Molecular Dynamics (MD) simulation using the GROMACS software package improved each 3D structure model, resulting in optimized and refined structures: RF1, RF2, and RF3. PROCHECK and ERRAT programs were employed to assess the structure quality. The Ramachandran plots from PROCHECK indicated that 100% of residues were within the allowed regions for all servers except for I-TASSER. For the refined structures, RF1 and RF3, all residues were concentrated within the allowed regions. According to the ERRAT program, the RF1 model exhibited the highest overall quality factor of 97.701, followed by RF3 and AlphaFold models with scores of 94.643 and 93.750, respectively. After these validations, RF1 emerged as the most accurately predicted 3D structure of HE4 and has one tunnel identified by CAVER 3.0 tool that facilitates the transportation of small particles to the active site, supported by FTsite and PrankWeb binding site predictions. This model holds potential for various computational studies, including the development of OC diagnostic kits. It will enhance our comprehension of the interactions between the protein and other biomolecules.

Docking and Simulation Studies on Novel Analogues of 3,4,5-Trihydroxy Benzoic Acid as HSP90Alpha Inhibitors

Abstract: HSP90 assists as a crucial molecular chaperone that responds to environmental stressors and helps in the survival of cells in microorganisms. This protein is integral to the stress response, aiding in the stabilization of various proteins essential for microbial survival. Consequently, the ability of a number of tissues to adjust to endogenous stress depends critically on appropriate chaperone activity. Modulators of chaperone activity, however, have emerged as a novel and developing area of drug discovery due to the association between changed chaperone function and the development of numerous illnesses. Inhibition of HSP90alpha can disrupt proper protein folding, thus impairing growth and virulence in fungi. In this work, we selected novel leads of gallic acid derivatives with the help of OSIRIS Property Explorer and DruLiTo software. Selected leads were subjected to ADME-T studies for further screening. Docking and molecular simulation studies on selected compounds were performed using Schrodinger v21 and GROMACS software to predict the bioactivity of novel leads of 3,4,5 trihydroxy benzoic acid for suppression of the HSP90alpha enzyme. Compounds 4N, 18N, 15N, and 14N showed good docking scores of -6.5, -6.4, -5.91, and -5.98, respectively, which was comparable to standard ciprofloxacin. Compound 4N and compound 14N demonstrated notable binding interactions and were selected for further investigation through molecular dynamics studies with HSP90alpha (PDB ID: 1YC1). RMSD, H BOND, and RMSF analysis confirmed the stable binding of compounds 4N and 14 N with the HSP90 enzyme. The RMSF plot showed less than 0.35 nm fluctuation for the HSP90alpha enzyme in complex with different ligands. It can be concluded that ligand binding can cause stability to the conformation of the protein. Compounds 4N and 14N are considered to be the best theoretical lead, which can further be studied experimentally as HSP90 alpha inhibitors for antimicrobial activity. other: Ongoing research aims to uncover more insights into the specific mechanisms of action, optimize structural features for enhanced efficacy, and explore potential synergies with existing antimicrobial agents. As a result, these derivatives hold promise as candidates for the development of novel antimicrobial agents with a broad spectrum of activity against bacteria and fungi

Molecular Dynamics Simulations and Binding Free Energy Calculations to Discover New Insights into NLRP3 Inhibitors

Background: Inflammation is an immunological reaction against an aggressor agent. NLRP3 inflammasome is a component of the immune system, which, when excessively activated, results in several inflammatory diseases, making it an attractive target for discovering antiinflammatory drugs. Computer-Aided Drug Design (CADD) techniques are powerful tools used to search for new drugs in less time and financial cost. Recently, studies demonstrated the CADD methods to discover information about NLRP3 inhibitors MCC950 and NP3-146. In addition, the discovery of GDC-2394 and its evaluation in clinical trials instigate new studies to find binding modes and structural attributes that can used in drug design works against this target. Objectives: Here, molecular modeling methods were used to discover the significant interactions of GDC-2394, MCC950, and NP3-146 with NLRP3 to obtain helpful information in drug design compared to other inhibitors. Methods: Molecular docking was performed using GOLD software. The best complexes were submitted into molecular dynamics simulations using GROMACS software, and the MM-PBSA was used to provide the free binding energy, which was performed using the tool g_mmpbsa compiled in GROMACS. Results: The RMSD, RMSF, Rg, SASA, and H-bond plots showed that the compound was stable during MD simulation time (100 ns) for GDC-2394. The PCA analysis for all compounds verified similar variance of the complex with the inhibitors to the apo-NLRP3, indicative of stability. DCCM analysis showed the best correlation in residues 134 - 371 region, which contains critical amino acids from the binding site (Ala227, Ala228, and Arg578), besides the newly identified residues. Using MMPBSA to provide the binding free energy, it was observed that the high affinity of the drugs against NLRP3 is related to the lower rigidity of the structure. Furthermore, we identified the critical residues Phe575, Pro352, Tyr632, and Met661 related to the coupling process. result: Then, RMSD, RMSF, Rg, SASA, and H-bond plots showed that the compound was stable during MD simulation time (100 ns) for GDC-2394. The PCA analysis for all compounds verified similar variance of the complex with the inhibitors to the apo-NLRP3, indicative of stability. Through DCCM analysis, the best correlation was observed in residues 134 - 371 region, which contains critical amino acids from the binding site (Ala227, Ala228, and Arg578), besides the newly identified residues. Using MM-PBSA to provide the binding free energy, it was observed that the high affinity of the drugs against NLRP3 is related to the lower rigidity of the structure. Furthermore, we identified the critical residues Phe575, Pro352, Tyr632, and Met661 related to the coupling process. Conclusion: Thus, these discoveries may contribute to the development of new anti-inflammatory drugs, such as NLRP3 inhibitors.

The active components of Erzhi wan and their anti-Alzheimer's disease mechanisms determined by an integrative approach of network pharmacology, bioinformatics, molecular docking, and molecular dynamics simulation

Erzhi Wan (EZW), a classic Traditional Chinese Medicine formula, has shown promise as a potential therapeutic option for Alzheimer's disease (AD), yet its mechanism remains elusive. Herein, we employed an integrative in-silico approach to investigate the active components and their mechanisms against AD. We screened four active components with blood-brain barrier permeabilities from TCMSP, along with 307 corresponding targets predicted by SwissTargetPrediction, PharmMapper, and TCMbank websites. Then, we retrieved 2260 AD-related targets from Genecards, OMIM, and NCBI databases. Furthermore, we constructed the protein-protein interaction (PPI) network of the intersected targets via the STRING database and performed the GO and KEGG enrichment analyses using the “clusterProfiler” R package. The results showed that the intersected targets were intimately related to the p53/PI3K/Akt signaling pathway, serotonergic synapse, and response to oxygen level. Subsequently, 25 core targets were found differentially expressed in brain regions by bioinformatics analyses of GEO datasets of clinical samples from the Alzdata database. The binding sites and stabilities between the active components and the core targets were investigated by the molecular docking approach using Autodock 4.2.6 software, followed by pocket detection and druggability assessment via the DoGSiteScorer server. The results showed that acacetin, β-sitosterol, and 3-O-acetyldammarenediol-II strongly interacted with the druggable pockets of AR, CASP8, POLB, and PREP. Eventually, the docking results were further cross-referenced with the literature research and validated by 100 ns of molecular dynamics simulations using GROMACS software. Binding free energies were calculated via MM/PBSA strategy combined with interaction entropy. The simulation results indicated stable bindings between four docking pairs including acacetin-AR, acacetin-CASP8, β-sitosterol-POLB, and 3-O-acetyldammarenediol–II–PREP. Overall, our study demonstrated a theoretical basis for how three active components of EZW confer efficacy against AD. It provides a promising reference for subsequent research regarding drug discoveries and clinical applications.

Calculating transmembrane voltage on the electric pulse-affected cancerous cell membrane: using molecular dynamics and finite element simulations.

Electroporation is a technique that creates electrically generated pores in the cell membrane by modifying transmembrane potential. In this work, the finite element method (FEM) was used to examine the induced transmembrane voltage (ITV) of a spherical-shaped MCF-7 cell, allowing researchers to determine the stationary ITV. A greater ITV than the critical value causes permeabilization of the membrane. Furthermore, the present study shows how a specific surface conductivity can act as a stand-in for the thin layer that constitutes a cell membrane as the barrier between extracellular and intracellular environments. Additionally, the distribution of ITV on the cell membrane and its maximum value were experimentally evaluated for a range of applied electric fields. Consequently, the entire cell surface area was electroporated 66% and 68% for molecular dynamics (MD) simulations and FEM, respectively, when the external electric field of 1500 V/cm was applied to the cell suspension using the previously indicated numerical methods. Furthermore, the lipid bilayers' molecular structure was changed, which led to the development of hydrophilic holes with a radius of 1.33 nm. Applying MD and FEM yielded threshold values for transmembrane voltage of 700 and 739 mV, respectively. Using MD simulations of palmitoyloleoyl-phosphatidylcholine (POPC), pores in cell membranes exposed to external electric fields were numerically investigated. The dependence on the electric field was estimated and developed, and the amount of the electroporated cell surface area matches the applied external electric field. To investigate more, a mathematical model based on an adaptive neuro-fuzzy inference system (ANFIS) is employed to predict the percent cell viability of cancerous cells after applying four pulses during electroporation. For MD simulations, ArgusLab, VMD, and GROMACS software packages were used. Moreover,for FEM analysis, COMSOL software package was used.Also, it is worth mentioning that for mathematical model, MATLAB software is used.

A Practical Guide to All-Atom and Coarse-Grained Molecular Dynamics Simulations Using Amber and Gromacs: A Case Study of Disulfide-Bond Impact on the Intrinsically Disordered Amyloid Beta.

Intrinsically disordered proteins (IDPs) pose challenges to conventional experimental techniques due to their large-scale conformational fluctuations and transient structural elements. This work presents computational methods for studying IDPs at various resolutions using the Amber and Gromacs packages with both all-atom (Amber ff19SB with the OPC water model) and coarse-grained (Martini 3 and SIRAH) approaches. The effectiveness of these methodologies is demonstrated by examining the monomeric form of amyloid-β (Aβ42), an IDP, with and without disulfide bonds at different resolutions. Our results clearly show that the addition of a disulfide bond decreases the β-content of Aβ42; however, it increases the tendency of the monomeric Aβ42 to form fibril-like conformations, explaining the various aggregation rates observed in experiments. Moreover, analysis of the monomeric Aβ42 compactness, secondary structure content, and comparison between calculated and experimental chemical shifts demonstrates that all three methods provide a reasonable choice to study IDPs; however, coarse-grained approaches may lack some atomistic details, such as secondary structure recognition, due to the simplifications used. In general, this study not only explains the role of disulfide bonds in Aβ42 but also provides a step-by-step protocol for setting up, conducting, and analyzing molecular dynamics (MD) simulations, which is adaptable for studying other biomacromolecules, including folded and disordered proteins and peptides.

Chemoinformatics Study of Benzodiazepine-1, 2, 3-triazole Derivatives Targeting Butyrylcholinesterase.

This study aims to assess the potential bioactivity of newly designed benzodiazepine-1,2,3-triazole derivatives using in-silico methodologies, with a primary focus on elucidating their inhibitory interactions with the butyrylcholinesterase (BuChE) enzyme, which is implicated in Alzheimer's disease. We employed multiple linear regression (MLR) methods to conduct a quantitative structure-activity relationship (QSAR) analysis on a collection of 31 benzodiazepine-1,2,3-triazole derivatives, with the goal of investigating, assessing, and predicting their activities, as well as designing novel compounds. This approach yielded highly accurate results, with coefficients of determination (R²) of 0.77 and 0.81 for the training and test datasets, respectively. Additionally, the optimized compounds were subjected to an Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) analysis, demonstrating their potential as non-hepatotoxic agents with enhanced absorption and blood-brain barrier permeability. To further validate these findings, the most favorable docking conformations were analyzed using molecular dynamics (MD) simulations with GROMACS software, predicting the stability of the formed complexes. These simulations underscored the critical role of hydrogen bonds in stabilizing the compounds at the BuChE receptor binding site. The results hold great promise for the development of innovative benzodiazepine-1,2,3-triazole derivatives as effective BuChE inhibitors, potentially leading to therapeutic interventions for Alzheimer's disease.

Exploring okra-derived compounds as prospective aromatase inhibitors: a computational study for enhanced breast cancer therapy

Estrogen receptor-positive breast cancer represents itself as the most prevalent malignancy among postmenopausal women. One of the promising therapeutic approaches involves the use of Aromatase inhibitors, which competitively bind to Aromatase, reducing estrone and estradiol levels. While current drugs have improved survival rates, they are not without adverse effects. Consequently, this study explores the computational screening of medicinally relevant compounds derived from okra (Abelmoschus esculentus) for potential Aromatase inhibition. Molecular docking employing AMDock v1.5.2 was utilized to assess binding affinities with Aromatase (PDB:3EQM). Subsequently, in-depth molecular interactions were examined using Discovery Studio Visualizer v4.5, and the stability of docked complexes was evaluated via molecular dynamics with the GROMACS package, focusing on RMSD, RMSF, H-bond count, SASA, Free energy landscape, Principal Component Analysis and binding affinity assessment. The pharmacokinetic properties of the okra compounds were predicted using admetSAR v2.0. Our findings highlight Quercetin 3-gentiobioside as a standout candidate, demonstrating superior binding affinity (-10 kcal/mol) and an estimated Ki of 46.77 nM compared to letrozole and other okra compounds. Molecular dynamic analysis confirms the stability of Quercetin 3-gentiobioside binding in terms of H-bonds and conformational integrity. In conclusion, our computational investigation identifies Quercetin 3-gentiobioside, along with Quercetin 3-O-rutinoside and Hyperin, as promising candidates for preclinical studies in the pursuit of potential Aromatase inhibitors. Communicated by Ramaswamy H. Sarma

Molecular Docking and Dynamic Simulation of Erythrina fusca Lour Chemical Compounds Targeting VEGFR-2 Receptor for Anti-Liver Cancer Activity

Liver cancer is a serious health concern characterized by abnormal cell growth, but currently, available treatment options are limited, suggesting the need for a new therapeutic method. Therefore, this research aimed to investigate the potential of chemical compounds obtained from the cangkring plant (Erythrina fusca) as anti-liver cancer agents targeting Vascular Endothelial Growth Factor Receptor 2 (VEGFR-2). The investigation was conducted in silico through molecular docking and dynamic method. Molecular docking was performed using AutoDock Tools, followed by visualization with Biovia Discovery Studio. Additionally, molecular dynamics simulation was conducted using GROMACS software and visualized with Grace. A total of 36 chemical compounds from E. fusca were used as ligands for molecular docking. The results showed that Isobavachalcone (ISB) was the most effective test compound with a binding energy of -11.45 kcal/mol, compared to the positive control Sorafenib with a value of - 11.51 kcal/mol. In this context, hydrogen bonding, as well as hydrophobic, electrostatic, and unfavorable molecular interactions were identified. Moreover, molecular dynamics simulation provided RMSD, RMSF, Radius of Gyration (Rg), and hydrogen bond parameters. Analysis of these parameters further confirmed the superior stability of ISB in binding to VEGFR-2, suggesting the potential to suppress angiogenesis by blocking the receptor.

In silico assessment of biocompatibility and toxicity: molecular docking and dynamics simulation of PMMA-based dental materials for interim prosthetic restorations

Aim: This study aimed to comprehensively assess the biocompatibility and toxicity profiles of poly(methyl methacrylate) (PMMA) and its monomeric unit, methyl methacrylate (MMA), crucial components in dental materials for interim prosthetic restorations. Methodology: Molecular docking was employed to predict the binding affinities, energetics, and steric features of MMA and PMMA with selected receptors involved in bone metabolism and tissue development, including RANKL, Fibronectin, BMP9, NOTCH2, and other related receptors. The HADDOCK standalone version was utilized for docking calculations, employing a Lamarckian genetic algorithm to explore the conformational space of ligand-receptor interactions. Furthermore, molecular dynamics (MD) simulations over 100 nanoseconds were conducted using the GROMACS package to evaluate dynamic actions and structural stability. The LigandScout was utilized for pharmacophore modeling, which employs a shape-based screening approach to identify potential ligand binding sites on protein targets. Results: The molecular docking studies elucidated promising interactions between PMMA and MMA with key biomolecular targets relevant to dental applications. MD simulation results provided strong evidence supporting the structural stability of PMMA complexes over time. Pharmacophore modeling highlighted the significance of carbonyl and hydroxyl groups as pharmacophoric features, indicating compounds with favorable biocompatibility profiles. Conclusion: This study underscores the potential of PMMA in dental applications, emphasizing its structural stability, molecular interactions, and safety considerations. These findings lay a foundation for future advancements in dental biomaterials, guiding the design and optimization of materials for enhanced biocompatibility. Future directions include experimental validation of computational findings and the development of PMMA-based dental materials with improved biocompatibility and clinical performance.Graphical

MolDy: molecular dynamics simulation made easy.

Molecular dynamics (MD) is a computational experiment that is crucial for understanding the structure of biological macro and micro molecules, their folding, and the inter-molecular interactions. Accurate knowledge of these structural features is the cornerstone in drug development and elucidating macromolecules functions. The open-source GROMACS biomolecular MD simulation program is recognized as a reliable and frequently used simulation program for its precision. However, the user requires expertise, and scripting skills to carrying out MD simulations. We have developed an end-to-end interactive MD simulation application, MolDy for Gromacs. This front-end application provides a customizable user interface integrated with the Python and Perl-based logical backend connecting the Linux shell and Gromacs software. The tool performs analysis and provides the user with simulation trajectories and graphical representations of relevant biophysical parameters. The advantages of MolDy are (i) user-friendly, does not requiring the researcher to have prior knowledge of Linux; (ii) easy installation by a single command; (iii) freely available for academic research; (iv) can run with minimum configuration of operating systems; (v) has valid default prefilled parameters for beginners, and at the same time provides scope for modifications for expert users. MolDy is available freely as compressed source code files with user manual for installation and operation on GitHub: https://github.com/AIBResearchMolDy/Moldyv01.git and on https://aibresearch.com/innovations.

Molecular Docking, Molecular Dynamics Simulation, and Analysis of EGFR-derived Peptides against the EGF

Background: The epidermal growth factor receptor (EGFR) is a member of the tyrosine kinase receptor family known as ErbB. The EGFR signaling pathway is an important regulator of cell proliferation, differentiation, division, and survival, as well as cancer development in humans. Epidermal growth factor, betacellulin, amphiregulin, transforming growth factor and heparin-binding EGF-like growth factor are high-affinity ligands of EGFR. Objective: Tumor progression can be effectively prevented by inhibiting EGF/EGFR interactions. In this study, many anti-EGF peptides targeting EGFR binding regions were designed, modeled, and evaluated. After selecting the peptides with the highest binding energy to the EGF, the interactions between the candidate peptides and all of the key EGFR ligands were investigated. Methods: To identify an EGF-binding peptide capable of blocking EGFR-EGF interactions, large-scale peptide mutation screening was performed. Using the AntiCP server, several possible peptides with anticancer properties were identified. The ClusPro analysis was performed in order to analyze the interactions between EGF and all of the library peptides. A total of five peptides with favorable docking scores were identified. The stability of three peptides with the best docking scores in complex with EGF was verified, applying molecular dynamics simulation with the help of the GROMACS software package. Finally, the interaction of candidate peptides with transforming growth factor-alpha, heparin-binding EGF-like growth factor, and betacellulin was investigated using the ClusPro server. Results: After the screening of modeled peptides by the ClusPro server and GROMACS software, two anti-EGF peptides of Pep4 and Pep5 with 31 residues were developed. Then, we demonstrate that both of these peptides can bind to the other high-affinity ligands of EGFR and block TGFA/EGFR, HBEGF/EGFR, and BTC/EGFR interactions. Conclusion: The findings suggest novel insights for developing therapies based on peptides for inhibiting the EGF, TGFA, HBEGF, and BTC signaling cascade in cancer cells. Pep4 and Pep5 designed in this work, are recommended as potentially promising anticancer peptides for further experimental evaluation.